AI Associate Data for AI Practice Question
A data engineer is troubleshooting a predictive model that stopped updating. The data flow from Data Cloud shows 'Data Transform Failed' with error: 'Field Amount cannot be null'. What is the most likely cause?
⚠ Common exam trap
Salesforce often tests the distinction between source-side constraints (validation rules) and target-side constraints (field nullability in the model schema), leading candidates to incorrectly choose Option B when the error actually originates from the target field requirement.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The target field in the model requires a non-null value but source data has nulls.
The error 'Field Amount cannot be null' indicates that the target field in the predictive model is configured to require a non-null value. When the data flow attempts to write records with null Amount values into that field, the transform fails. This is a common schema constraint mismatch where the source data contains nulls that violate the target field's nullability requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The data transform includes a filter that removes records with null Amount.
Why it's wrong here
A filter would remove nulls, not cause a failure; the error suggests nulls are being processed.
- ✗
The source object has a validation rule.
Why it's wrong here
Validation rules apply to UI record creation/update, not data flows.
- ✗
The data flow schedule is incorrect.
Why it's wrong here
The error is data-related, not schedule-related.
- ✓
The target field in the model requires a non-null value but source data has nulls.
Why this is correct
This directly matches the error: the transform requires non-null input.
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